macedonizer
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Browse files- README.md +66 -0
- added_tokens.json +1 -0
- blaze-koneski.jpg +0 -0
- config.json +32 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.json +0 -0
README.md
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---
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language:
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- mk
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thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg
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license: Apache 2.0
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datasets:
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- wiki-mk
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- time-mk-news-2010-2015
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---
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# mk-gpt2
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Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
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Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
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[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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and first released at [this page](https://openai.com/blog/better-language-models/).
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## Model description
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mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This
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means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
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of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
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it was trained to guess the next word in sentences.
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More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
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shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
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predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
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This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
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useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
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prompt.
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### How to use
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Here is how to use this model to get the features of a given text in PyTorch:
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import random
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from transformers import AutoTokenizer, AutoModelWithLMHead
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tokenizer = AutoTokenizer.from_pretrained('macedonizer/mk-gpt2') \
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model = AutoModelWithLMHead.from_pretrained('macedonizer/mk-gpt2')
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input_text = 'Скопје е '
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if len(input_text) == 0: \
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encoded_input = tokenizer(input_text, return_tensors="pt") \
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output = model.generate( \
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bos_token_id=random.randint(1, 50000), \
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do_sample=True, \
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top_k=50, \
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max_length=1024, \
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top_p=0.95, \
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num_return_sequences=1, \
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) \
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else: \
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encoded_input = tokenizer(input_text, return_tensors="pt") \
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output = model.generate( \
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**encoded_input, \
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bos_token_id=random.randint(1, 50000), \
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do_sample=True, \
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top_k=50, \
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max_length=1024, \
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top_p=0.95, \
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num_return_sequences=1, \
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)
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decoded_output = [] \
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for sample in output: \
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decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
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print(decoded_output)
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added_tokens.json
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{"<|endoftext|>": 52000, "<|beginoftext|>": 52001, "<PAD>": 52002}
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blaze-koneski.jpg
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config.json
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{
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"_name_or_path": "../../models/mk-gpt2",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 0,
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"embd_pdrop": 0.1,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"resid_pdrop": 0.1,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.9.1",
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"use_cache": true,
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"vocab_size": 52003
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b3a81e38ee1b4a5a90f3e229bff5c81483e78269919e8b4f1e1d96847e370667
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size 515767529
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special_tokens_map.json
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{"bos_token": "<|beginoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<unk>", "pad_token": "<PAD>", "mask_token": "<mask>"}
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tokenizer_config.json
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{"errors": "replace", "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "special_tokens_map_file": null, "name_or_path": "../../models/mk-gpt2", "tokenizer_file": "../../models/mk-gpt2\\tokenizer.json", "tokenizer_class": "GPT2Tokenizer"}
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vocab.json
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